User:Chase-san/KohonenMap

This is my implementation of a Self-organizing map.

This and all my other code in which I display on the robowiki falls under the ZLIB License.

How to use

//Create the map, 16 nodes, 2 input, 1 output
KohonenMap map = new KohonenMap(new int[]{16},2,1);

//Initializing gives every input and output a random value, which allows the Kohonen map to work.
map.initialize();

//For example a KohonenMap can be used for binary values
//A larger map is recommended for better output

//XOR
//0 0	0
//0 1	1
//1 0	1
//1 1	0

double[][] input = new double[][] {
		{0,0}, {0,1}, {1,0}, {1,1}	
};

double[][] output = new double[][] {
		{0}, {1}, {1}, {0}
};
//To train your map
for(int i = 0; i < input.length; ++i) {
	//find the BMU you want to train
	map.findInputBMU(input[i]);
	//and then tell the main to train on that BMU
	map.train(input[i], output[i]);
}

//to get meaningful data from the map
for(int i = 0; i < input.length; ++i) {
	//find the BMU
	map.findInputBMU(input[i]);
	
	//get the output for that BMU
	double out = map.getOutput()[0];
	
	//You can simply round to get the desired binary output
	System.out.println(Arrays.toString(input[i]) + " " + Math.round(out) + " " + out);
}

org.csdgn.maru.util.KohonenMap

package org.csdgn.maru.util;

import java.util.Random;
 
/**
 * A Self-Organizing Map implementation.
 * 
 * Requires: <br>
 * org.csdgn.nn.DensityFunction<br>
 * org.csdgn.nn.DistanceFunction<br>
 * org.csdgn.nn.density.StandardDensity<br>
 * org.csdgn.nn.distance.EulerDistanceSquared<br><br>
 * 
 * Train The Map<br>
 * findInputBMU(double[])<br>
 * train(double[])<br><br>
 * Find Output<br>
 * findInputBMU(double[])<br>
 * getOutput()
 * 
 */
public class KohonenMap {
	/**
	 * Holds the neighborhood layout;
	 */
	private final Node[] map;
	private final int[] mapSize;
	private double learningRate = 0.8;
	private Density density;
	private Distance distance;
	private Distance neighborhood;
	private boolean wrap = false;
	private int BMU;
 
	private double cutoff = 1e-4;
 
	/**
	 * @param mapSize
	 *            Size of the neighborhood. Example: {10,10} produces a 2
	 *            dimensional map, each dimension having 10 nodes. Total nodes
	 *            would be 100.
	 * @param input
	 *            The length of the input vector (1D only)
	 * @param output
	 *            The length of the output vector (1D only)
	 */
	public KohonenMap(int[] mapSize, int input, int output) {
		/* Setup the map */
		int size = 1;
		for (int m : mapSize)
			size *= m;
 
		this.map = new Node[size];
 
		this.mapSize = mapSize.clone();
 
		this.density = new Density.Simple();
		this.distance = new Distance.EulerSq();
		this.neighborhood = new Distance.Manhattan();
 
		int[] pos = new int[mapSize.length];
		for (int i = 0; i < map.length; ++i) {
			this.map[i] = new Node(mapSize.length, input, output);
			/* Setup the location of each node, for speed reasons. */
			System.arraycopy(pos, 0, this.map[i].position, 0, pos.length);
 
			/* Update the position marker */
			++pos[0];
			for (int j = 0; j < pos.length - 1; ++j) {
				if (pos[j] >= mapSize[j]) {
					++pos[j + 1];
					pos[j] = 0;
				}
			}
		}
	}
 
	/**
	 * Initializes the map to random values
	 */
	public void initialize() {
		Random r = new Random();
		initialize(r);
	}
 
	/**
	 * Initializes the map with the given random function. Uses the nextDouble
	 * function.
	 */
	public void initialize(Random random) {
		for (Node n : map) {
			for (int i = 0; i < n.input.length; ++i)
				n.input[i] = random.nextDouble();
			for (int i = 0; i < n.output.length; ++i)
				n.output[i] = random.nextDouble();
		}
	}
 
	/**
	 * Finds the Best Matching Unit for the given input.
	 * 
	 * @return the BMUs identifier
	 */
	public int findInputBMU(double[] input) {
		BMU = 0;
 
		double distance = Double.MAX_VALUE;
		for (int i = 0; i < map.length; ++i) {
			double dist = this.distance.distance(map[i].input, input);
 
			if (dist < distance) {
				distance = dist;
				BMU = i;
			}
		}
		return BMU;
	}
 
	/**
	 * Finds the Best Matching Unit for the given output.
	 * 
	 * @return the BMUs identifier
	 */
	public int findOutputBMU(double[] output) {
		BMU = 0;
		double distance = Double.MAX_VALUE;
		for (int i = 0; i < map.length; ++i) {
			double dist = this.distance.distance(map[i].output, output);
			if (dist < distance) {
				distance = dist;
				BMU = i;
			}
		}
		return BMU;
	}
 
	/**
	 * Finds the Worst Matching Unit for the given input
	 * 
	 * @return the WMUs identifier
	 */
	public int findInputWMU(double[] input) {
		BMU = 0;
		double distance = Double.MIN_VALUE;
		for (int i = 0; i < map.length; ++i) {
			double dist = this.distance.distance(map[i].input, input);
			if (dist > distance) {
				distance = dist;
				BMU = i;
			}
		}
		return BMU;
	}
 
	/**
	 * Finds the Worst Matching Unit for the given output
	 * 
	 * @return the WMUs identifier
	 */
	public int findOutputWMU(double[] output) {
		BMU = 0;
		double distance = Double.MIN_VALUE;
		for (int i = 0; i < map.length; ++i) {
			double dist = this.distance.distance(map[i].output, output);
			if (dist > distance) {
				distance = dist;
				BMU = i;
			}
		}
		return BMU;
	}
 
	/**
	 * Sets the Matched index to the set value.
	 * 
	 * @param index
	 */
	public void setMatchIndex(int index) {
		BMU = Math.max(0, Math.min(index, map.length - 1));
	}
 
	/**
	 * This returns the input of the last found BMU or WMU.
	 * 
	 * @return the input vector
	 */
	public double[] getInput() {
		return this.map[BMU].input;
	}
 
	/**
	 * This returns the output of the last found BMU or WMU.
	 * 
	 * @return the output vector
	 */
	public double[] getOutput() {
		return this.map[BMU].output;
	}
 
	/**
	 * This returns the input of the given ID.
	 * 
	 * @return the input vector
	 */
	public double[] getInput(int id) {
		if (id >= 0 && id < map.length)
			return this.map[id].input;
		return null;
	}
 
	/**
	 * This returns the output of the given ID.
	 * 
	 * @return the output vector
	 */
	public double[] getOutput(int id) {
		if (id >= 0 && id < map.length)
			return this.map[id].output;
		return null;
	}
 
	/**
	 * Sets the learning rate of this KohonenMap
	 * 
	 * @param rate
	 *            value between 0 and 1
	 */
	public void setLearningRate(double rate) {
		learningRate = Math.max(Math.min(rate, 1), 0);
	}
 
	/**
	 * Returns the current rate of learning
	 * 
	 * @return the learning rate
	 */
	public double getLearningRate() {
		return learningRate;
	}
 
	/**
	 * Sets the map to wrap its updates (slightly more costly)
	 */
	public void setWraps(boolean doesWrap) {
		wrap = doesWrap;
	}
 
	/**
	 * Returns if the current map wraps
	 * 
	 * @return
	 */
	public boolean isWrapping() {
		return wrap;
	}
 
	/**
	 * Gets the current cutoff density.
	 */
	public double getCutoff() {
		return cutoff;
	}
 
	/**
	 * The cutoff density in which under a node will not be trained.
	 * @param cutoff
	 */
	public void setCutoff(double cutoff) {
		this.cutoff = cutoff;
	}
 
	/**
	 * Sets the density function this map uses for updating nearby nodes. If
	 * unset it uses the StandardDensity class.
	 * 
	 * @param func
	 *            the Density Function
	 */
	public void setDensityFunction(Density func) {
		this.density = func;
	}
 
	/**
	 * Sets the distance function used to find the best or worst matching unit.
	 * If unset, this map uses the EulerDistanceSquared class.<br>
	 * The neighborhood distance is Manhattan Distance.
	 * 
	 * @param func
	 */
	public void setDistanceFunction(Distance func) {
		this.distance = func;
	}
	
	/**
	 * Sets the distance function used to calculate the neighborhood distance between nodes. 
	 * @param func
	 */
	public void setNeighborhoodDistanceFunction(Distance func) {
		this.neighborhood = func;
	}
 
	/**
	 * Updates the map with the given data. Uses the last found BMU or WMU.
	 * 
	 * @param input
	 *            input vector
	 * @param output
	 *            expected output vector
	 */
	public void train(double input[], double output[]) {
		Node bmu = map[BMU];
		for (int i = 0; i < map.length; ++i) {
			map[i].update(bmu.position, input, output);
		}
	}
 
	private class Node {
		/** Location in the neighborhood */
		private final int[] position;
		/** Input vector */
		private final double[] input;
		/** Output vector */
		private final double[] output;
 
		public Node(int mapSize, int inputSize, int outputSize) {
			position = new int[mapSize];
			input = new double[inputSize];
			output = new double[outputSize];
		}
 
		private double train(double c, double t, double n) {
			return c + n * (t - c) * learningRate;
		}
 
		private void update(int[] pos, double[] in, double[] out) {
			double distance = neighborhood.distance(pos, position);
			if (wrap) {
				int[] tpos = pos.clone();
				int[] npos = position.clone();
				for (int i = 0; i < tpos.length; ++i) {
					tpos[i] += mapSize[i] / 2;
					npos[i] += mapSize[i] / 2;
					if (tpos[i] > mapSize[i])
						tpos[i] -= mapSize[i];
					if (npos[i] > mapSize[i])
						npos[i] -= mapSize[i];
				}
				double ndist = neighborhood.distance(tpos, npos);
				if (ndist < distance)
					distance = ndist;
			}
 
			double neighborhood = density.density(distance);
 
			/* Changes below this point benefits are negligible */
			if (neighborhood < cutoff)
				return;
 
			for (int i = 0; i < input.length; ++i)
				input[i] = train(input[i], in[i], neighborhood);
 
			for (int i = 0; i < output.length; ++i)
				output[i] = train(output[i], out[i], neighborhood);
		}
 
	}
	
	public static abstract class Density {
		/**
		 * Calculates the density at the given point, where x is a certain distance from the center of the distribution.
		 */
		public abstract double density(double x);
	 
		public static class Normal extends Density {
			private final double multi;
			private final double variance;
			private final double mean;
			public Normal() {
				this(1,0);
			}
			public Normal(double variance, double mean) {
				this.multi = 1.0 / Math.sqrt(2*Math.PI*variance);
				this.variance = 2.0*variance;
				this.mean = mean;
			}
			@Override
			public double density(double x) {
				double e = ((x - mean)*(x - mean)) / variance;
				return multi*Math.exp(-e);
			}
		}
	 
		public static class Simple extends Density {
			/**
			 * <math>density(x) = 2^{-x^2}</math>
			 */
			@Override
			public double density(double x) {
				return Math.pow(2, -(x*x));
			}
		}
	}

	public static abstract class Distance {
		public abstract double distance(double[] p, double[] q);
		public double distance(int[] p, int[] q) {
			double[] dp = new double[p.length];
			double[] dq = new double[q.length];
			for(int i=0;i<p.length;++i)
				dp[i] = p[i];
			for(int i=0;i<p.length;++i)
				dq[i] = q[i];
			return distance(dp,dq);
		}
		
		public static class Manhattan extends Distance {
			/**
			 * Calculates the manhatten distance between the two points.
			 */
			@Override
			public double distance(double[] p, double[] q) {
				if (p == null || q == null)
					return 0;
				int len = Math.min(p.length, q.length);
				int output = 0;
				for (int i = 0; i < len; ++i)
					output += Math.abs(p[i] - q[i]);
				return output;
			}
		}
	 
		public static class EulerSq extends Distance {
			/**
			 * <math>distSqr(p,q) = \sum_{i=0}^n (p_i - q_i)^2</math> where
			 * <math>n</math> is the size of the smaller of <math>p</math> or
			 * <math>q</math>
			 */
			@Override
			public double distance(double[] p, double[] q) {
				if (p == null || q == null)
					return 0;
				int len = Math.min(p.length, q.length);
				double k, output = 0;
				for (int i = 0; i < len; ++i)
					output += (k = (p[i] - q[i])) * k;
				return output;
			}
		}
	 
		public static class Euler extends EulerSq {
			/**
			 * <math>dist(p,q) = \sqrt_{\sum_{i=0}^n (p_i - q_i)^2}</math> where
			 * <math>n</math> is the size of the smaller of <math>p</math> or
			 * <math>q</math>
			 */
			@Override
			public double distance(double[] p, double[] q) {
				return Math.sqrt(super.distance(p, q));
			}
		}
	}
}